Gaining a deeper understanding of weather and being able to predict its
future conduct have always been considered important endeavors for the growth
of our society. This research paper explores the advancements in understanding
and predicting nature's behavior, particularly in the context of weather
forecasting, through the application of machine learning algorithms. By
leveraging the power of machine learning, data mining, and data analysis
techniques, significant progress has been made in this field. This study
focuses on analyzing the contributions of various machine learning algorithms
in predicting precipitation and temperature patterns using a 20-year dataset
from a single weather station in Dhaka city. Algorithms such as Gradient
Boosting, AdaBoosting, Artificial Neural Network, Stacking Random Forest,
Stacking Neural Network, and Stacking KNN are evaluated and compared based on
their performance metrics, including Confusion matrix measurements. The
findings highlight remarkable achievements and provide valuable insights into
their performances and features correlation.

该研究通过应用机器学习算法，分析了各种算法在使用达卡市一个气象站 20 年数据预测降水和温度变化模式方面的贡献和性能指标。研究结果突出了显著成就，并提供了有价值的洞察和特征相关性。

机器学习模型对天气预测的比较评估

Comparative Evaluation of Weather Forecasting using Machine Learning  Models

Most state-of-the-art approaches for weather and climate modeling are based
on physics-informed numerical models of the atmosphere. These approaches aim to
model the non-linear dynamics and complex interactions between multiple
variables, which are challenging to approximate. Additionally, many such
numerical models are computationally intensive, especially when modeling the
atmospheric phenomenon at a fine-grained spatial and temporal resolution.
Recent data-driven approaches based on machine learning instead aim to directly
solve a downstream forecasting or projection task by learning a data-driven
functional mapping using deep neural networks. However, these networks are
trained using curated and homogeneous climate datasets for specific
spatiotemporal tasks, and thus lack the generality of numerical models. We
develop and demonstrate ClimaX, a flexible and generalizable deep learning
model for weather and climate science that can be trained using heterogeneous
datasets spanning different variables, spatio-temporal coverage, and physical
groundings. ClimaX extends the Transformer architecture with novel encoding and
aggregation blocks that allow effective use of available compute while
maintaining general utility. ClimaX is pre-trained with a self-supervised
learning objective on climate datasets derived from CMIP6. The pre-trained
ClimaX can then be fine-tuned to address a breadth of climate and weather
tasks, including those that involve atmospheric variables and spatio-temporal
scales unseen during pretraining. Compared to existing data-driven baselines,
we show that this generality in ClimaX results in superior performance on
benchmarks for weather forecasting and climate projections, even when
pretrained at lower resolutions and compute budgets.

该研究介绍了一种名为 ClimaX 的深度学习模型，能够使用异构数据集（跨越不同变量，时空范围和物理基础）进行训练，预测气象和气候。ClimaX 扩展了 Transformer 架构，并表现出优越的性能。

ClimaX：天气和气候的基础模型

ClimaX: A foundation model for weather and climate

Scene recovery is a fundamental imaging task for several practical
applications, e.g., video surveillance and autonomous vehicles, etc. To improve
visual quality under different weather/imaging conditions, we propose a
real-time light correction method to recover the degraded scenes in the cases
of sandstorms, underwater, and haze. The heart of our work is that we propose
an intensity projection strategy to estimate the transmission. This strategy is
motivated by a straightforward rank-one transmission prior. The complexity of
transmission estimation is $O(N)$ where $N$ is the size of the single image.
Then we can recover the scene in real-time. Comprehensive experiments on
different types of weather/imaging conditions illustrate that our method
outperforms competitively several state-of-the-art imaging methods in terms of
efficiency and robustness.

本文提出了一种实时的场景恢复方法，通过一种基于强度投影策略的传输预估方法，在不同的天气 / 成像条件下，如沙尘暴、水下和雾霾等情况下提高视觉质量，并证明了该方法在效率和鲁棒性方面优于其他竞争的成像方法。

基于一阶特征约束的场景实时恢复

Rank-One Prior: Toward Real-Time Scene Recovery

Predicting unseen weather phenomena is an important issue for disaster
management. In this paper, we suggest a model for a convolutional
sequence-to-sequence autoencoder for predicting undiscovered weather situations
from previous satellite images. We also propose a symmetric skip connection
between encoder and decoder modules to produce more comprehensive image
predictions. To examine our model performance, we conducted experiments for
each suggested model to predict future satellite images from historical
satellite images. A specific combination of skip connection and
sequence-to-sequence autoencoder was able to generate closest prediction from
the ground truth image.

本文提出了一种卷积序列到序列自编码器模型，结合编码器和解码器模块之间的对称跳跃连接，预测先前卫星图像中未发现的天气情况，实验结果表明该模型的性能良好。